Abstract
We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.
| Original language | English |
|---|---|
| Article number | 4237471 |
| Journal | Complexity |
| Volume | 2018 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
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All Science Journal Classification (ASJC) codes
- General
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Forecasting Financial Crashes : Revisit to Log-Periodic Power Law. / Dai, Bingcun; Zhang, Fan; Tarzia, Domenico; Ahn, Kwangwon.
In: Complexity, Vol. 2018, 4237471, 01.01.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Forecasting Financial Crashes
T2 - Revisit to Log-Periodic Power Law
AU - Dai, Bingcun
AU - Zhang, Fan
AU - Tarzia, Domenico
AU - Ahn, Kwangwon
PY - 2018/1/1
Y1 - 2018/1/1
N2 - We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.
AB - We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.
UR - http://www.scopus.com/inward/record.url?scp=85054546353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054546353&partnerID=8YFLogxK
U2 - 10.1155/2018/4237471
DO - 10.1155/2018/4237471
M3 - Article
AN - SCOPUS:85054546353
VL - 2018
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 4237471
ER -